DECODE
importedsoftware/decode
This is the official implementation of our publication "Deep learning enables fast and dense single-molecule localization with high accuracy" (Nature Methods)
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- GPL-3.0(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- TuragaLab
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/TuragaLab/DECODE
- Documentation
- unknown
- Tags
- deep-learning · gpu · high-density · localization-microscopy · microscopy · pytorch · smlm
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- REALMlocalization-microscopy · microscopy · smlm
Adaptive Optics plugin for MicroManager
- Investigation of the synaptic ultrastructure with multicolor STORMsmlm · localization-microscopy
We are interested in the spatial distribution of proteins at the presynaptic terminal and how the molecular composition of the terminal regulates the synaptic vesicle cycle. Single-molecule…
- picassomicroscopy · smlm
A collection of tools for painting super-resolution images
- DeconvOptim.jlgpu · microscopy
A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs.
- fastddmgpu · microscopy
Python library for Differential Dynamic Microscopy analysis
- PyHoloscopegpu · microscopy
Optimised python package for digital holographic microscopy, both inline and off-axis, with GPU support.
- api.github.com/repos/TuragaLab/DECODEretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2023-06-22, 123 stars, license reported as GPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/15.json→ .entries["decode"]
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